To learn more about how bioregulatory networks control the cell cycle in normal and cancer cells, we collaborate with a cross-disciplinary team to generate electronic molecular interaction maps (MIMs), which show the behavior of cell cycle regulatory pathways during normal growth and under conditions that perturb the cell cycle. These efforts help develop bioinformatics tools that organize large collections of facts, including descriptions of networks of interacting regulatory molecules, multi-protein complexes, protein modifications (e.g., phosphorylations), etc. One of the main stumbling blocks to organizing molecular knowledge is the lack of a common language that allows scientists to integrate data in a clear, standardized, and preferably computer-readable format. To that end, we implemented the Molecular Interaction Map (MIM) language, a diagrammatic annotation first proposed by Kurt Kohn, which encodes molecular information in the form of diagrams (molecular interaction maps or MIMs). These MIMs are used to represent and analyze molecular interactions in the same way that circuit diagrams are used to trouble-shoot electronic devices. Investigators usually describe biochemical pathways in cartoon-like diagrams, but these representations of molecular interactions are often incomplete and ambiguous. For example, an arrow between two components could signify an increase in quantity, an increase in activity, or a modification of one molecule by the other. In addition, enzymes in bioregulatory networks are often substrates of other enzymes, and molecules are often subject to modifications that change their binding or enzymatic capabilities. Moreover, regulatory proteins can form multi-molecular complexes, which have different activities, depending on their composition and modifications. Finally, each domain within a regulatory molecule may have its own binding, modification, and/or enzymatic functions. Thus, a molecule's activity and interaction capabilities may depend on its modification state, and on the other molecules to which it may be bound. All of these interactions must be taken into account for a full understanding of the system. In the MIM language, we use a small number of defined, unambiguous graphical symbols to portray each type of molecular interaction. Each molecule is represented in a single place in a diagram, and interactions between molecules are specified by arrows or bars at the end of connecting lines. Because modified molecules and multi-molecular complexes may have different properties than the original molecules, the outcome of each interaction (such as a phosphorylated molecule, or a multi-molecular complex) is depicted as a circle, or "node" on an interaction line. These nodes are treated in a way that allows them to form more interactions and extend the network. The symbols and conventions used in the language, as well as examples of MIMs, can be accessed at our Web site: http://discover.nci.nih.gov/mim and in an article describing the principles of the MIM language. The graphical MIM language allows a simultaneous view of many interactions involving any given molecule. It can portray competing interactions, which are common in bioregulatory networks. An interested researcher can trace all the interactions of a given molecule from a single location. Readers can look up a molecule in a glossary, or in the electronic (eMIM) diagrams, a mouse-click on the molecule name opens links to more information. Each interaction is labeled with a link to an annotated description, which includes links to cited references. The interested researcher can read the annotations to gain in-depth information on each molecular interaction, or browse the various maps to become acquainted with the general concept of how cells regulate a particular metabolic process. For example, the eMIM depicting the early stages in DNA replication features all the possible molecular interactions between molecules involved in the process;additional maps represent subsets of interactions that occur during specific stages of the cell cycle and in response to cellular stress. We compile and update maps of the major biological control systems, and work to post them on a Web site for the use of the general public and to integrate them in a concise manner. We may then discern common patterns of molecular interaction logic that give bioregulatory networks their remarkable flexibility and robustness. To elucidate the logic of signaling pathways from the multitude of molecular interactions depicted in the MIMs, we are interacting with a multidisciplinary group of researchers to develop MIM-based computer simulations. Such tools will illustrate the processes by which cells govern DNA replication and cell cycle progression and may help us understand the perturbations in cell cycle progression that occur in cancer cells and underlie the sensitivity of these cells to anti-tumor drugs.